Optical die to database inspection

ABSTRACT

Methods and systems for detecting defects on a wafer are provided. One system includes one or more computer subsystems configured for generating a rendered image based on information for a design printed on the wafer. The rendered image is a simulation of an image generated by an optical inspection subsystem for the design printed on the wafer. Generating the rendered image includes one or more steps, and the computer subsystem(s) are configured for performing at least one of the one or more steps by executing a generative model. The computer subsystem(s)) are also configured for comparing the rendered image to an optical image of the wafer generated by the optical inspection subsystem. The design is printed on the wafer using a reticle. In addition, the computer subsystem(s) are configured for detecting defects on the wafer based on results of the comparing.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention generally relates to methods and systems for detectingdefects on a wafer by optical die to database inspection.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Fabricating semiconductor devices such as logic and memory devicestypically includes processing a substrate such as a semiconductor waferusing a large number of semiconductor fabrication processes to formvarious features and multiple levels of the semiconductor devices. Forexample, lithography is a semiconductor fabrication process thatinvolves transferring a pattern from a reticle to a resist arranged on asemiconductor wafer. Additional examples of semiconductor fabricationprocesses include, hut are not limited to, chemical-mechanical polishing(CMP), etch, deposition, and ion implantation. Multiple semiconductordevices may be fabricated in an arrangement on a single semiconductorwafer and then separated into individual semiconductor devices.

Inspection processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield in the manufacturing process and thus higher profits. Inspectionhas always been an important part of fabricating semiconductor devicessuch as ICs. However, as the dimensions of semiconductor devicesdecrease, inspection becomes even more important to the successfulmanufacture of semiconductor devices.

Many reticle inspection methods detect defects on reticles usingdie-to-database type comparisons. Such inspection typically involvesacquiring a microscope image of a reticle. From a database thatdescribes the intended pattern on the reticle, an image that theinspection microscope is expected to observe of that reticle may becalculated or simulated. The acquired optical image may then be comparedto the calculated or simulated image to detect defects on the reticle.Such reticle inspection methods have proven useful for a number of uses.However, such reticle inspection methods are not capable of findingprocess-induced defects (i.e., defects that would be printed on a waferdue to the interaction between the reticle and the process of printingthe reticle on the wafer).

Some reticle inspections are performed using wafers that have beenprinted with the reticles. In this manner, defects that are detected onthe wafer can be used to determine if there are defects on the reticlethat was used to print the wafer. Some such inspections are performed onoptical platforms by comparing an inspected image frame to a referenceframe, where the reference frame is a sample of the image generated fromthe wafer. Examples for reference image frames are: images from adjacentdies, images from a standard reference die on the same wafer or adifferent wafer, and images from the adjacent cells (in an arraystructure).

Currently, die to database inspection performed for wafers exists onlyon a scanning electron microscope (SEM) inspection platform. However,due to the throughput constraints (e.g., due to the physics of electronbeam tools), only a substantially small number of locations (i.e., notthe entire wafer and not entire dies on the wafer) can be checked. Inaddition, inspection performed by electron beam inspection of wafers istoo slow to qualify every reticle for which qualification is needed.Furthermore, with the advent of multiple patterning step lithographyprocesses, and as a result needing multiple reticle qualifications for asingle lithography process, the number of reticles for whichqualification must be performed should grow substantially.

The currently available optical inspection methodologies that involvecomparing wafer images to a reference wafer image to detect defects on awafer cannot serve some of the use cases for which such inspection isperformed. For example, such currently used optical inspections cannotdetect repeater defects within dies printed with a single die reticle.One example of such a use case is for extreme ultraviolet (EUV) maskqualification. In particular, due to the lack of a pellicle, particleson the mask when printed on the wafer become repeater defects on thewafer. Therefore, such defects will cancel each other out in die-to-diecomparisons and not be detected. In addition, such currently usedoptical inspections cannot be used for design intent checks. Forexample, a reference image generated from part of a wafer contains theprocess variation. Therefore, comparison of such a reference image witha different wafer image will cancel out the process variation in bothimages rendering the process variation undetectable. Furthermore, beforethe process becomes mature, it is difficult to find a “golden” referencedie. For example, a user may have no idea which die or dies can be usedas a “golden” reference die for comparison with other dies on a wafer.

Accordingly, it would be advantageous to develop systems and/or methodsfor detecting defects on a wafer that do not have one or more of thedisadvantages described above.

SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construedin any way as limiting the subject matter of the appended claims.

One embodiment relates to a system configured to detect defects on awafer. The system includes an optical inspection subsystem that includesat least a light source and a detector. The light source is configuredto generate light that is directed to a wafer. The detector isconfigured to detect light from the wafer and to generate outputresponsive to the detected light. The system also includes one or morecomputer subsystems configured for generating a rendered image based oninformation for a design printed on the wafer. The rendered image is asimulation of an image generated by the optical inspection subsystem forthe design printed on the wafer. Generating the rendered image includesone or more steps. The one or more computer subsystems are configuredfor performing at least one of the one or more steps by executing agenerative model. The computer subsystem(s) are also configured forcomparing the rendered image to an optical image of the wafer generatedby the optical inspection subsystem. The design is printed on the waferusing a reticle. The computer subsystem(s) are further configured fordetecting defects on the wafer based on results of the comparing. Thesystem may be further configured as described herein.

Another embodiment relates to a computer-implemented method fordetecting defects on a wafer. The method includes steps for each of thefunctions of the one or more computer subsystems described above. Thesteps of the method are performed by one or more computer systems. Themethod may be performed as described further herein. In addition, themethod may include any other step(s) of any other method(s) describedherein. Furthermore, the method may be performed by any of the systemsdescribed herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for detecting defects on awafer. The computer-implemented method includes the steps of the methoddescribed above. The computer-readable medium may be further configuredas described herein. The steps of the computer-implemented method may beperformed as described further herein. In addition, thecomputer-implemented method for which the program instructions areexecutable may include any other step(s) of any other method(s)described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent uponreading the following detailed description and upon reference to theaccompanying drawings in which:

FIG. 1 is a schematic diagram illustrating a side view of an embodimentof a system configured as described herein;

FIG. 2 is a flow chart illustrating one embodiment of steps that may beperformed by one or more computer subsystems described herein;

FIGS. 2a-2b are schematic diagrams illustrating various embodiments of agenerative model that may be included in and/or used by the embodimentsdescribed herein;

FIGS. 3-6 are flow charts illustrating various embodiments of steps thatmay be performed by one or more computer subsystems described herein;and

FIG. 7 is a block diagram illustrating one embodiment of anon-transitory computer-readable medium storing program instructionsexecutable on a computer system for performing one or more of thecomputer-implemented methods described herein.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein he described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The terms “design” and “design data” as used herein generally refer tothe physical design (layout) of an IC and data derived from the physicaldesign through complex simulation or simple geometric and Booleanoperations. The design may include any other design data or design dataproxies described in commonly owned U.S. Pat. No. 7,570,796 issued onAug. 4, 2009 to Zafar et al. and U.S. Pat. No. 7,676,077 issued on Mar.9, 2010 to Kulkarni et al., both of which are incorporated by referenceas if fully set forth herein. In addition, the design data can bestandard cell library data, integrated layout data, design data for oneor more layers, derivatives of the design data, and full or partial chipdesign data.

In general, however, the design information or data cannot be generatedby is imaging a wafer with a wafer inspection system. For example, thedesign patterns formed on the wafer may not accurately represent thedesign for the wafer and the wafer inspection system may not be capableof generating images of the design patterns formed on the wafer withsufficient resolution such that, the images could be used to determineinformation about the design for the wafer. Therefore, in general, thedesign information or design data cannot be generated using a physicalwafer. In addition, the “design” and. “design data” described hereinrefers to information and data that is generated by a semiconductordevice designer in a design process and is therefore available for usein the embodiments described herein well in advance of printing of thedesign on any physical wafers.

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals. Unlessotherwise noted herein, any of the elements described and shown mayinclude any suitable commercially available elements.

One embodiment relates to a system configured to detect defects on awafer. In general, the embodiments described herein are configured foroptical die-to-database (DB) inspection of wafers performed using a deeplearning (DL) technique. In other words, the embodiments describedherein are generally configured for comparing an optical image of awafer printed with a reticle to a rendered image generated from a DBusing one or more DL, engines to detect defects on the wafer.

The wafer may include any wafer known in the art. The design is printedon the wafer using a reticle. The design may be printed on the waferusing the reticle in any suitable manner known in the art (e.g., bydepositing one or more materials on a wafer and performing a lithographyprocess on the wafer to transfer the design from the reticle to thewafer). The wafer may also be a short loop wafer, meaning a wafer onwhich not all process steps required to ultimately form a functioningdevice have been performed. In other words, the wafer may or may not bea full loop wafer. For example, the wafer may be a wafer on which onlythe process steps described above (e.g., deposition, lithography, andpossibly etch) have been performed. As such, the wafer may not includeone or more layers (patterned and/or unpatterned) formed under the layerof the wafer being inspected. In this manner, the process steps that areperformed on the wafer prior to the inspection described herein mayinclude only those required to transfer a design for the wafer from areticle to the wafer. The reticle may include any reticle known in theart such as reticles configured for use with extreme ultraviolet (EUV)light or another suitable type of light.

One embodiment of such a system is shown in FIG. 1. The system includesan optical inspection subsystem that includes at least a light sourceand a detector. The light source is configured to generate light that isdirected to a wafer. The detector is configured to detect light from thewafer and to generate output responsive to the detected light.

In the embodiment of the system shown in FIG. 1, optical inspectionsubsystem 10 includes an illumination subsystem configured to directlight to wafer 14. The illumination subsystem includes at least onelight source. For example, as shown in FIG. 1, the illuminationsubsystem includes light source 16. In one embodiment, the illuminationsubsystem is configured to direct the light to the wafer at one or moreangles of incidence, which may include one or more oblique angles and/orone or more normal angles. For example, as shown in FIG. 1, light fromlight source 16 is directed through optical element 18 and then lens 20to beam splitter 21, which directs the light, to wafer 14 at a normalangle of incidence. The angle of incidence may include any suitableangle of incidence, which may vary depending on, for instance,characteristics of the wafer and the defects to be detected on thewafer.

The illumination subsystem may be configured to direct the light to thewafer at different angles of incidence at different times. For example,the inspection subsystem may be configured to alter one or morecharacteristics of one or more elements of the illumination subsystemsuch that the light can be directed to the wafer at an angle ofincidence that is different than that shown in FIG. 1. In one suchexample, the inspection subsystem may be configured to move light source16, optical element 18, and lens 20 such that the light is directed tothe wafer at a different angle of incidence.

In some instances, the inspection subsystem may be configured to directlight to the wafer at more than one angle of incidence at the same time.For example, the illumination subsystem may include more than oneillumination channel, one of the illumination channels may include lightsource 16, optical element 18, and lens 20 as shown in FIG. 1 andanother of the illumination channels (not shown) may include similarelements, which_(—) may be configured differently or the same, or mayinclude at least a light source and possibly one or more othercomponents such as those described further herein. If such light isdirected to the wafer at the same time as the other light, one or morecharacteristics (e.g., wavelength, polarization, etc.) of the lightdirected to the wafer at different angles of incidence may be differentsuch that light resulting from illumination of the wafer at thedifferent angles of incidence can be discriminated from each other atthe detector(s),

In another instance, the illumination subsystem may include only onelight source (e.g., source 16 shown in FIG. 1) and light from the lightsource may be separated into different optical paths (e.g., based onwavelength, polarization, etc.) by one or more optical elements (notshown) of the illumination subsystem. Light in each of the differentoptical paths may then be directed to the wafer. Multiple illuminationchannels may be configured to direct light to the wafer at the same timeor at different times (e.g., when different illumination channels areused to sequentially illuminate the wafer). In another instance, thesame illumination channel may be configured to direct light to the waferwith different characteristics at different times. For example, in someinstances, optical element 18 may be configured as a spectral filter andthe properties of the spectral filter can be changed in a variety ofdifferent ways (e.g., by swapping out the spectral filter) such thatdifferent wavelengths of light can be directed to the wafer at differenttimes. The illumination subsystem may have any other suitableconfiguration known in the art for directing light having different orthe same characteristics to the wafer at different or the same angles ofincidence sequentially or simultaneously.

In one embodiment, light source 16 may include a broadband plasma (BBP)light source. In this manner, the light generated by the light sourceand directed to the wafer may include broadband light. However, thelight source may include any other suitable light source such as alaser. The laser may include any suitable laser known in the art and maybe configured to generate light at any suitable wavelength orwavelengths known in the art. In addition, the laser may be configuredto generate light that is monochromatic or nearly-monochromatic. In thismanner, the laser may be a narrowband laser. The light source may alsoinclude a polychromatic light source that generates light at multiplediscrete wavelengths or wavebands.

Light from optical element 18 may be focused to beam splitter 21 by lens20. Although lens 20 is shown in FIG. 1 as a single refractive opticalelement, it is to be understood that, in practice, lens 20 may include anumber of refractive and/or reflective optical elements that incombination focus the light from the optical element, to the wafer. Theillumination subsystem shown in FIG. 1 and described herein may includeany other suitable optical elements (not shown). Examples of suchoptical elements include, but are not limited to, polarizingcomponent(s), spectral filter(s), spatial filter(s), reflective opticalelement(s), apodizer(s), beam splitter(s), aperture(s), and the like,which may include any such suitable optical elements known in the art.In addition, the system may be configured to alter one or more of theelements of the illumination subsystem based on the type of illuminationto be used for inspection.

The inspection subsystem may also include a scanning subsystemconfigured to cause the light to be scanned over the wafer. For example,the inspection subsystem may include stage 22 on which wafer 14 isdisposed during inspection. The scanning subsystem may include anysuitable mechanical and/or robotic assembly (that includes stage 22)that can be configured to move the wafer such that the light can bescanned over the wafer. In addition, or alternatively, the inspectionsubsystem may he configured such that one or more optical elements ofthe inspection subsystem perform some scanning of the light over thewafer. The light may be scanned over the wafer in any suitable fashion.

The inspection subsystem further includes one or more detectionchannels. At least one of the one or more detection channels includes adetector configured to detect light from the wafer due to illuminationof the wafer by the inspection subsystem and to generate outputresponsive to the detected tight. For example, the inspection subsystemshown in FIG. 1 includes two detection channels, one formed by collector24, element 26, and detector 28 and another formed by collector 30,element 32, and detector 34. As shown in FIG. 1, the two detectionchannels are configured to collect and detect light at different anglesof collection. In some instances, one detection channel is configured todetect specularly reflected light, and the other detection channel isconfigured to detect light that is not specularly reflected (e.g.,scattered, diffracted, etc.) from the wafer. However, two or more of thedetection channels may be configured to detect the same type of lightfrom the wafer (e.g., specularly reflected light). Although FIG. 1 showsan embodiment of the inspection subsystem that includes two detectionchannels, the inspection subsystem may include a different number ofdetection channels (e.g., only one detection channel or two or moredetection channels). Although each of the collectors are shown in FIG. 1as single refractive optical elements, it is to be understood that eachof the collectors may include one or more refractive optical element(s)and/or one or more reflective optical element(s).

The one or more detection channels may include any suitable detectorsknown in the art. For example, the detectors may includephoto-multiplier tubes (PMTs), charge coupled devices (CCDs), and timedelay integration (TDI) cameras. The detectors may also include anyother suitable detectors known in the art. The detectors may alsoinclude non-imaging detectors or imaging detectors. In this manner, ifthe detectors are non-imaging detectors, each of the detectors may beconfigured to detect certain characteristics of the scattered light suchas intensity but may not be configured to detect such characteristics asa function of position within the imaging plane. As such, the outputthat is generated by each of the detectors included in each of thedetection channels of the inspection subsystem may be signals or data,but not image signals or image data. In such instances, a computersubsystem such as computer subsystem 36 of the system may be configuredto generate images of the wafer from the non-imaging output of thedetectors. However, in other instances, the detectors may be configuredas imaging detectors that are configured to generate imaging signals orimage data. Therefore, the system may be configured to generate theoutput described herein in a number of ways.

It is noted that FIG. 1 is provided herein to generally illustrate aconfiguration of an inspection subsystem that may be included in thesystem embodiments described herein. Obviously, the inspection subsystemconfiguration described herein may be altered to optimize theperformance of the system as is normally performed when designing acommercial inspection system. In addition, the systems described hereinmay be implemented using an existing inspection system (e.g., by addingfunctionality. described herein to an existing inspection system) suchas the 28xx and 29xx series of tools that are commercially availablefrom KLA-Tencor. For some such systems, the methods described herein maybe provided as optional functionality of the system (e.g., in additionto other functionality of the system). Alternatively, the systemdescribed herein may he designed “from scratch” to provide a completelynew system.

The optical inspection subsystem shown in FIG. 1 may also be configuredas an optical defect review subsystem by alteration of one or moreparameters of the inspection subsystem (e.g., to increase the resolutionof the inspection subsystem), which may be performed in any suitablemanner known in the art. In this manner, the systems described hereinmay be configured to perform defect review and/or classification usingcomparisons such as those described herein. Defect review and/orclassification may otherwise be performed in any suitable manner knownin the art.

Computer subsystem 36 of the system may be coupled to the detectors ofthe inspection subsystem in any suitable manner (e.g., via one or moretransmission media, which may include “wired” and/or “wireless”transmission media) such that the computer subsystem can receive theoutput generated by the detectors during scanning of the wafer. Computersubsystem 36 may be configured to perform a number of functions usingthe output of the detectors as described herein and any other functionsdescribed further herein. This computer subsystem may he furtherconfigured as described herein.

This computer subsystem (as well as other computer subsystems describedherein) may also be referred to herein as computer system(s). Each ofthe computer subsystem(s) or system(s) described herein may take variousforms, including a personal computer system, image computer, mainframecomputer system, workstation, network appliance, Internet appliance, orother device. In general, the term “computer system” may be broadlydefined to encompass any device having one or more processors, whichexecutes instructions from a memory medium. The computer subsystem(s) orsystem(s) may also include any suitable processor known in the art suchas a parallel processor. In addition, the computer subsystem(s) orsystem(s) may include a computer platform with high speed processing andsoftware, either as a standalone or a networked tool.

If the system includes more than one computer subsystem, then thedifferent computer subsystems may be coupled to each other such thatimages, data, information, instructions, etc. can be sent between thecomputer subsystems as described further herein. For example, computersubsystem 36 may be coupled to computer subsystem(s) 102 (as shown bythe dashed line in FIG. 1) by any suitable transmission media, which mayinclude any suitable wired and/or wireless transmission media known inthe art. Two or more of such computer subsystems may also be effectivelycoupled by a shared computer-readable storage medium (not shown).

The one or more computer subsystems described above are configured forgenerating a rendered image based on information for a design printed onthe wafer. The rendered image is a simulation of an image generated bythe optical inspection subsystem for the design printed on the wafer.Generating the rendered image includes one or more steps, and the one ormore computer subsystems are configured for performing at least one ofthe one or more steps by executing a generative model. For example, asshown in FIG. 2, the computer subsystem(s) may be configured to acquiredesign database 200, which may include design polygons 202 or otherdesign information described herein. The computer subsystem(s) may usegenerative model(s) 204 to generate rendered image 206 from designdatabase 200. Each of the generative models may perform image renderingwith a deep learning technique. The generative model(s) and the deeplearning technique(s) may include any of those described further herein.In this manner, a deep learning approach can be applied to generate arendered image from design. Therefore, the computer subsystem(s) may usedesign polygons 202 as input to generative model(s) that output renderedimage 206, one of example of which is shown in FIG. 2 as rendered image208.

In this manner, the computer subsystem(s) are configured for generatinga simulated optical inspection image from design. In addition, thecomputer subsystem(s) are configured for generating a simulated opticalimage from design using one or more generative models to perform one ormore steps involved in generating the simulated image. The generativemodeling can be performed in a number of different manners describedfurther herein. For example, the computer subsystem(s) may use agenerative model to model from design (polygons) with estimated nearfield and an accurate or approximate optical system model. In anotherexample, the computer subsystem(s) may use a generative model to modelfrom a stack of geometry and material information to calculate orestimate the near field and to learn and use an accurate or approximateoptical system model.

The computer subsystem(s) are also configured for comparing the renderedimage to an optical image of the wafer generated by the opticalinspection subsystem and detecting defects on the wafer based on resultsof the comparing. The design is printed on the wafer using a reticle.For example, as shown in FIG. 2, physical wafer 210 may be printed withthe design for the wafer using the reticle. In one such example, design212 may be printed on wafer 210. Imaging hardware 214 (i.e., an opticalinspection subsystem as described herein) may then generate opticalimage 216 of the physical wafer. One example of such an optical image isshown in FIG. 2 as optical image 218. The computer subsystem(s) may thenperform compare and detect, as shown in step 220, by comparing renderedimage 206 and optical image 216 and detecting defects based on thecomparison results. For example, the computer subsystem(s) may subtractrendered image 208 from optical image 218 thereby generating differenceimage 222. In this manner, the computer subsystem(s) may be configuredfor comparing the optical wafer image to a rendered image from design.

The computer subsystem(s) may then detect defects on the wafer in anysuitable manner using the difference image. For example, the computersubsystem(s)) may apply one or more defect detection algorithms and/ormethods to the difference image. In one such example, the computersubsystem(s) may compare signals or data in difference image 222 to athreshold. Any of the signals or data that are above the threshold maybe identified as defects or potential defects, while any of the signalsor data that are below the threshold may not be identified as defects orpotential defects. Of course, many other defect detection algorithms andmethods are known in the art, and the embodiments described herein arenot limited to any one defect detection algorithm or method. In otherwords, the results of the comparison described herein may be input toany suitable defect detection algorithm and/or method known in the art.

In some embodiments, the one or more computer subsystems are furtherconfigured for determining if the reticle passes qualification based onthe detected defects. Determining if the reticle passes qualificationbased on the defects detected on the wafer may be performed in anysuitable manner known in the art. One advantage of the embodimentsdescribed herein is that they can perform die-to-DB inspection for EUVmask qualification. Different from normal optical masks, an EUV maskqualification system is not currently available because of the lack ofactinic EUV mask inspection systems. However, the embodiments describedherein may be used for reticle qualification of any type of reticleknown in the art. In this manner, the printability of the reticle onwafer may be validated through the die-to-DB optical wafer inspectiondescribed herein as part of reticle qualification.

The embodiments described herein may also be configured for performingprocess window qualification (PWQ) such as that described in U.S. Pat.No. 6,902,855 to Peterson et al. issued on Jun. 7, 2005, U.S. Pat. No.7,418,124 to Peterson et al. issued on Aug. 26, 2008, U.S. Pat. No.7,769,225 to Kekare et al. issued on Aug. 3, 2010, U.S. Pat. No.8,041,106 to Pak et al. issued on Oct. 18, 2011, and U.S. Pat. No.8,213,704 to Peterson et al. issued on Jul. 3, 2012, which areincorporated by reference as if fully set forth herein. The embodimentsdescribed herein may include any step(s) of any method(s) described inthese patents and may be further configured as described in thesepatents. A PWQ wafer may be printed as described in these patents.

In another embodiment, the one or more computer subsystems areconfigured for determining a performance of one or more process stepsperformed on the wafer based on the detected defects. In one suchembodiment, the wafer is a short loop wafer. For example, a short loopwafer may be manufactured with only a subset of all of the layers thatwould be formed on the wafer to fabricate a fully functioning device.Such wafers may be used to check only specific process steps such as alithography step and/or an etch step. The performance of the one or moreprocess steps performed on the wafer based on the detected defects maybe determined in any suitable manner. Once the performance of the one ormore process steps has been determined, the one or more process stepsmay be altered in any suitable manner (e.g., by determining and alteringone or more parameters of the one or more process steps based on thedetermined performance of the one or more process steps to therebycorrect the performance of the one or more process steps, e.g., to bringthe performance of the one or more process steps back intospecification).

The die-to-DB for optical wafer inspection described herein is newtechnology. In particular, currently, there is no optical die-to-DBmethodology available for wafer inspection. In addition, the die-to-DBfor optical wafer inspection has a number of important advantages overother currently available methods for detecting defects on wafers. Forexample, the embodiments described herein can detect die-to-die repeaterdefects and variations from design. In addition, the embodimentsdescribed herein do not rely on using a standard reference die generatedfrom a physical wafer for use as the DB to which wafer die images arecompared. For example, standard reference dies can be used as proxiesfor detecting die-to-die repeater defects. However, oftentimes, it isnot known Which die is suitable for use as a standard reference die.:Furthermore, there is no inspection technology available today to checkthe design intent.

Scanning electron microscope (SEM) die-to-DB inspection for wafers iscurrently available. SEM die-to-DB is commonly used today for severaluse cases such as for detection of critical dimension (CD) variation,where the sensitivity requirement can be as small as 2 nm. However, SEMdie-to-DB is not fast enough to serve the needs of wafer inspection. Forexample, currently, due to throughput constraints (due to the physics ofthe SEM imaging process), only a substantially small number of locationscan be checked. In contrast, the optical die-to-DB inspection describedherein can inspect an entire wafer within an acceptable time period. Inthis manner, the optical die-to-DB inspection described herein can beperformed much faster than SEM die-to-DB. In addition, the die-to-DBinspection described herein can be performed for any wafer known in theart and for qualifying any reticle known in the art. Therefore, theembodiments described herein enable users to do integration debugging ofany new reticle that they may not have time to do with the currentelectron beam solutions. In addition, since SEM has substantially higherresolution than optical inspection tools and only images the top layerof a wafer, rendering a SEM image from design is relatively easy. Forexample, SEM images can look substantially similar to design except thatthe corners may be rounded. In addition, SEM die-to-DB inspection canhave challenges in detection for nuisance issues.

In comparison, optical die-to-DB is a much more difficult problem thanSEM die-to-DB due to optical limitations in resolution and requiredaccuracy and throughput in simulation to create a practical product. Dueto its technical difficulty, optical die-to-DB for wafer inspection isnot currently available in the industry.

Some reticle inspection methods use a near field approximation fordetecting defects on reticles. For example, the near field at thereticle plane may be approximated based on a thin film assumption andinformation about the optical path of the reticle inspection subsystem.This thin film assumption assumes the near field at the reticle plane isclose to design (for lithography only one layer), which holds whenwavelength and feature size are approximately the same. With today'sdesign rule, the feature size is much smaller than wavelength even forreticles, where the feature size is 4× the feature size on wafers.Therefore, reticle plane near field approximations are becoming more andmore challenging in reticle inspection. On wafers, it is even morechallenging due to the 4× shrink of feature size.

The generative model may be a deep learning (DL) type model. In thismanner, the embodiments described herein may be configured for opticaldie-to-DB wafer inspection with deep learning technique(s).

In one embodiment, the generative model is configured as a convolutionalneural network (CNN). In this manner, a CNN may be used as a DL engine.One embodiment of a CNN configuration that may be used in theembodiments described herein is shown in FIG. 2a . As shown in thisfigure, input image 224 may be input to first layer 226 of the CNN,layer 1. Layer 1 may include various kernels such as convolution kernel228, rectification and contrast normalization kernel 230, and poolingkernel 232 as shown in layer 1. The output of layer 1 may be input tosecond layer 234 of the CNN, layer 2. This layer may also includeconvolution kernel 236, rectification and contrast normalization kernel238, and pooling kernel 240. The output of this layer may be input toone or more additional layers (shown schematically in the figure by theellipsis) until the input of the next to the last layer (not shown inFIG. 2a ) is input to final layer 242 of the CNN, layer n. Final layer242 may include convolution kernel 244, rectification and contrastnormalization kernel 246, and pooling kernel 248. The output of thisfinal layer may be output image 250 that may be compared to target image252, which may be an optical image acquired by the optical inspectionsubsystem by imaging an actual wafer.

Cost function 254 may be used to determine differences between theoutput image and the target image and to modify one or more parametersof one or more layers of the CNN based on the differences. Examples ofcost functions that can be used for training of the CNN includeEuclidean distance, cross-entropy, and any other suitable cost functionsknown in the art. A backpropagation algorithm may minimize the costfunction and converge to an optimal network.

In this manner, the CNN may convert an input image to an output image.Each layer contains a convolution kernel with sets of convolutionkernels operating on the input image to the layer, and therectification/contrast normalization and pooling layers are optional.FIG. 2b shows an example of the details of each layer that may beincluded in a CNN used in the embodiments described herein. (FIG. 2b hasbeen excerpted from a figure in “Tutorial on Deep Learning for Vision,”CVPR 2014, which is incorporated by reference as if fully set forthherein. The generative models described herein may be further configuredas described in this document.) As shown in FIG. 2b , each layer of aCNN may include filter bank 256 to which image 258 is input. Filter bank256 performs convolution on the input image to generate set 260 ofsub-images. The sub-images are input to rectification and contrastnormalization kernel 262, which generates set 264 of sub-images. Thesub-images generated by the rectification and contrast normalizationkernel may then be input to pooling kernel 266. Output 268 of thepooling layer may then be provided to the next layer in the CNN. Thekernels included in each of the layers may have any suitableconfiguration known in the art. In addition, the CNN may include anysuitable number of layers known in the art.

In another embodiment, the generative model is configured as anauto-encoder. The auto-encoder may have any suitable configuration knownin the art. In addition, the DL engine can have any applicable DLarchitecture and their variations or implementations.

In one embodiment, the at least one step is performed by executing thegenerative model and an additional generative model. For example, a DLrendering engine may be used in multiple combinations of modeling stepsor in each single step with single and/or multiple DL engines. In onesuch embodiment, the one or more steps include a first step and a secondstep, and the one or more computer subsystems are configured forperforming the first step by executing the generative model andperforming the second step by executing the additional generative model.In this manner, different generative models may be used for differentsteps performed for generating the rendered image. The configurations ofthe different generative models will be dependent on the step(s) forwhich they will be used. In one such example, a different generativemodel may be used for each of the steps described herein that areperformed to generate a rendered image. In this manner, the output ofone generative model may be input to a different generative model. Insome instances though, the steps described herein may be performed usinga combination of one or more generative models and one or morenon-generative models. In one such instance, a generative model may beused to perform one step involved in generating the rendered image whilea non-generative model may be used to perform a different step involvedin generating the rendered image. The non-generative model may. includeany suitable model that can be configured to perform one or more stepsdescribed herein, some examples of which are described further herein.

In another such embodiment, the one or more computer subsystems areconfigured for separately training the generative model and theadditional generative model. For example, a DL rendering engine may beused in multiple combinations of modeling steps or in each single stepwith single and/or multiple DL engines with single or multipletrainings. As described further herein, when the generative model and/orone or more additional models are trained, rendered image(s) may hecompared with optical image(s) to determine differences between thoseimages and the differences are used to train the generative model and/orthe one or more additional models. When training generative model(s),the comparisons and/or the modifications to the generative models beingtrained may be performed using a cost function, which may include anysuitable cost function such as, but not limited to, Euclidean distanceand cross entropy.

The one or more steps that may be performed by executing a generativemodel or more than one generative model may include any of the step(s)described herein. For example, as described further herein, the one ormore steps include modeling an optical inspection image from design,which may be performed with DL using one or more of the generativemodels described herein. In addition, as described further herein, theone or more steps may include modeling from design (polygons) togenerate optical images from an inspection tool, which may be performedwith DL using one or more of the generative models described herein. Asalso described further herein, the one or more steps may includemodeling from a stack of geometry and material information to estimatenear field and/or an optical system model through a DL approach usingone or more of the generative models described herein.

In one embodiment, the one or more steps include converting polygons inthe information for the design to a gray scale image. For example, asshown in FIG. 3, design polygons 300 such as design polygons 302 may beconverted to gray scale image 306 by DB Raster 304. In particular, theDB Raster may convert polygons 300 to a gray scale image. DB Raster maybe performed in any suitable manner. For example, the design polygonsmay be used to generate an intermediate binary image. Downsampling andanti-aliasing may then be performed on the intermediate binary image togenerate the gray scale image. In this manner, gray scale image 306 maybe a raw gray scale image. Features in the raw gray scale image may havedimensions (e.g., CD) that are equal to the original design dimensions.

In one such embodiment, converting the polygons is performed withsub-pixel accuracy. In other words, the computer subsystem(s) mayconvert polygons in the design to a gray scale image with sub-pixelaccuracy. Sub-pixel accuracy means that the gray scale image should beable to represent polygons with dimensions (e.g., width or height)smaller than one pixel. :For example, if there is a polygon with aheight of 0.31 pixels, that height should be reflected in the gray scaleimage properly (as 0.31 pixels). In addition, the gray scale imageshould reflect the polygon position with sub-pixel accuracy. Forexample, if a first polygon is centered at 31.3 pixels and a secondpolygon is centered at 42.7 pixels, the gray scale image should be ableto represent the non-integer distance between the polygons, which is11.4 pixels (42.7 pixels−31.3 pixels). In contrast, many DB rastermethods in use today can only handle polygons with sizes that are aninteger of pixel number.

In another such embodiment, the one or more steps include generating amodified gray scale image by applying bias correction and cornerrounding to the gray scale image. For example, as shown in FIG. 3, DBmodel 308 may use gray scale image 306 as input to produce modified grayscale image 310. By applying the bias correction and corner rounding tothe gray scale image, the features shown in the modified gray scaleimage may have different dimensions and different corner rounding thanthe features shown in the gray scale image. For example, very often,real pattern sizes on wafers are different from the as-designed CDs dueto lithography process error. Such differences are generally referred toin the art as “bias.” Therefore, by modifying the gray scale image toaccount for such bias, the rendered images generated as described hereinwill simulate the optical images that would be produced by theinspection subsystem much more accurately. Appropriate bias correctionsand corner rounding may be acquired from any suitable source in anysuitable manner (e.g., experimentally or empirically). The bias may alsobe expressed in any suitable manner (e.g., as a constant for a constantbias or as a function for a nonlinear bias).

In some such embodiments, the one or more steps also include estimatinga near field of the wafer based on the modified gray scale image and theinformation for the design printed on the wafer. For example, as shownin FIG. 3, near field estimation 312 may use modified gray scale image310 as input to produce estimated near field 314. The information forthe design used for the near field estimation may include materialparameters, three dimensional (3D) effects, wavelength and angledependency, etc. The estimated near field of the wafer is a simulationof an electromagnetic (EM) field that would be generated at or near thetop surface of the wafer (i.e., at the wafer plane) by the interactionof the materials and their geometry on the wafer and the light directedto the wafer by the optical inspection subsystem. The estimated nearfield may include amplitude as shown in left side of estimated nearfield 314 and phase as shown in right side of estimated near field 314.The estimated near field may also be or include complex numbers. Theestimated near field may be estimated in a number of different manners.For example, the near field may be estimated by Kirchhoff/thin maskapproximation, thick mask approximation, rigorous EM simulation (e.g.,finite-difference time-domain (FDTD)), rigorous EM simulation added tothin mask approximation, and any other suitable approximation, function,or model known in the art.

In one such embodiment, the one or more steps include: generating aninitial rendered image that is another simulation of the image generatedby the optical inspection subsystem for the design printed on the waferbased on the near field and an optical model of the optical inspectionsubsystem. Therefore, in the embodiments described herein, the input tooptical modeling is the near field estimation and not the designdatabase. For example, as shown in FIG. 3, optical model 316 may useestimated near field 314 as input to generate initial rendered image318. The optical model may model with and without aberrations of theoptical inspection subsystem. Generating the initial rendered image maybe performed using an incoherent model, a Partial Coherence Model, thePartial Coherence-Hopkins Formulation, a linear convolution model, alinear convolution model with Hermitian quadratic form, Robust PrincipalComponent Analysis (Robust PCA), an Abbe imaging method, a rigorous EMmodel, and any other suitable approximation, function, or model known inthe art.

In a further embodiment, the one or more steps also include generatingthe rendered image from the initial rendered image by modifying theinitial rendered image to minimize differences between the initialrendered image and the optical image generated by the optical inspectionsubsystem. For example, as shown in FIG. 3, dynamic compensation 320performed by the computer subsystem(s) may modify initial rendered image318 to generate rendered image 322, one example of which is shown inFIG. 3 as rendered image 324. The dynamic compensation may be performedto compensate for differences between the rendered image and an opticalimage such as gain and offset of the images (e.g., for tone mismatchesand relatively small misalignment between the images) and differences inthe images due to aberrations of the optical inspection subsystem. Inaddition, since the simulations described herein may intentionally benot completely rigorous, and since the materials/dimensions of featuresprinted on the wafer may vary from design in unpredictable ways, runtime compensation may be performed to reduce differences between therendered image and the optical image. Smaller differences between therendered image and the optical image mean that the rendered image ismore similar to the optical image. In this manner, there will besignificantly fewer nuisances and false defects detected by performingthe dynamic compensation described herein.

In one such example, the gray scale of a rendered image may be slightlysmaller than the gray scale of an optical image, and/or the renderedimage may not be aligned with the optical image very well. So, simplycomparing the rendered image with the optical image and detectingdefects based on the comparison results may produce a significant numberof nuisance defects. Therefore, the dynamic compensation describedherein can be designed to reduce such systematic differences between therendered image and the optical image. Such differences in gray scaleand/or alignment can be dynamically compensated for using any suitablemodel, algorithm, or function known in the art. In another such example,tool aberration in the optical imaging can cause the real optical imageto be different from the expected optical image. Examples of such toolaberrations include, but are not limited to, lens decentering and waferdefocus. All aberrations can cause phase errors across the exit pupil ofthe imaging lens (compared with the expected ideal phase distribution).By far, the most common way to describe aberrations in optical imagingsystems is using Zemike polynomials. Therefore, the dynamic compensationdescribed herein may use a description of the aberrations such as Zemikepolynomials or any other suitable description of optical imaging systemaberrations known in the art to modify the rendered image to therebyminimize differences between the rendered image and the optical image.Such modification may be performed using any suitable model, algorithm,or function known in the art.

As further shown in FIG. 3, optical image 326, one example of which isshown in FIG. 3 as optical image 328, may be acquired by the computersubsystem(s) from the inspection subsystem (or a computer readablestorage medium in which the image was stored) and compared to renderedimage 322. Such comparisons may generate difference image 330. Thecomparisons between the rendered image and the optical image may beperformed for defect detection, which may be performed as describedfurther herein. The comparisons between the rendered image and theoptical image may also or alternatively be performed for one or moreother purposes. For example, in some instances, rendered and opticalimages corresponding to the same (or substantially the same) portions ofthe design may be compared for alignment of the rendered image (andtherefore the design) to the optical images. Such alignment may beperformed for a number of reasons including setting up optical die-to-DBinspection as well as other reasons such as setting up or modifying oneor more parameters used for dynamic compensation 320.

As described above, one or more generative models may be used to performone or more steps performed for generating the rendered image. In onesuch example, one or more generative models may be used for near fieldestimation 312 shown in FIG. 3. In this case, one or more DL engines maylearn EM fields on the surface of the wafer, which depend on materialparameters, 3D effects, illumination wavelength and angle, etc. Duringtraining and rendering, the dynamic compensation is optional. Inaddition, during training, a cost function (not shown in FIG. 3) such asone of those described further herein may he used to compare renderedimage 322 with optical image 326 and to modify one or more parameters ofthe DL engine(s) used for near field estimation. One or more othermodels may be used for all other steps shown in FIG. 3, and those one ormore other models may or may not be generative models.

In another such example, one or more generative models may be used foroptical model 316 shown in FIG. 3. In this case, the DL engine(s) maylearn the optical model with and without aberration from the system.During training and rendering, the dynamic compensation is optional. Inaddition, during training, a cost function (not shown in FIG. 3) such asone of those described further herein may be used to compare renderedimage 322 with optical image 326 and to modify one or more parameters ofthe DL engine(s) used for optical modeling. One or more other models maybe used for all other steps shown in FIG. 3, and those one or more othermodels may or may not be generative models.

In an additional such example, one or more generative models may be usedfor near field estimation 312 and optical model 316 shown in FIG. 3. Inthis case, one or more DL engines may learn both EM fields on thesurface of the wafer, which depend on material parameters, 3D effects,illumination wavelength and angle, etc, and the optical model with andwithout aberration from the system. In this manner, steps 312 and 316shown in FIG. 3 may not be performed as separate steps, but may heperformed as a single step whose input is modified gray scale image 310and whose output is initial rendered image 318. During training andrendering, the dynamic compensation is optional. In addition, duringtraining, a cost function (not shown in FIG. 3) such as one of thosedescribed further herein may be used to compare rendered image 322 withoptical image 326 and to modify one or more parameters of the DLengine(s) used for near field estimation and optical modeling. One ormore other models may be used for all other steps shown in FIG. 3, andthose one or more other models may or may not be generative models.

In a further example, one or more generative models may be used for DBmodel 308 shown in FIG. 3. In this case, the DL engine(s) may learn thebias of patterns printed on the wafer from design. Application of thebias step is use case dependent in the final die-to-DB comparison. Forexample, detecting CD variation requires no bias correction, or biascorrection to standard wafer. Particle detection preferably includesbias correction to reduce nuisance. During training and rendering, thedynamic compensation is optional. In addition, during training, a costfunction (not shown in FIG. 3) such as one of those described furtherherein may be used to compare rendered image 322 with optical image 326and to modify one or more parameters of the DL engine(s) used for the DBmodel. One or more other models may be used for all other steps shown inFIG. 3, and those one or more other models may or may not be generativemodels.

In yet another such example, one or more generative models may be usedfor DB Raster 304 and DB Model 308 shown in FIG. 3. In this case, one ormore DL engines generate the rendered design including bias correctionas input for EM and optical simulation (i.e., steps 312 and 316). Inthis manner, steps 304 and 308 shown in FIG. 3 may not be performed asseparate steps, but may be performed as a single step whose input isdesign polygons 300 and whose output is modified gray scale image 310.During training and rendering, the dynamic compensation is optional. Inaddition, during training, a cost function (not shown in FIG. 3) such asone of those described further herein may be used to compare renderedimage 322 with optical image 326 and to modify one or more parameters ofthe DL engine(s) used for DB Raster 304 and DB Model 308. One or moreother models may be used for all other steps shown in FIG. 3, and thoseone or more other models may or may not be generative models.

In still another such example, one or more generative models may be usedfor DB Raster 304, DB Model 308, near field estimation 312, and opticalmodel 316 shown in FIG. 3. In this case, one or more DL engines simulatethe entire image rendering process. In this manner, steps 304, 308, 312,and 316 shown in FIG. 3 may not be performed as separate steps, but maybe performed as a single step whose input is design polygons 300 andwhose output is initial rendered image 318. During training andrendering, the dynamic compensation is optional. In addition, duringtraining, a cost function (not shown in FIG. 3) such as one of thosedescribed further herein may be used to compare rendered image 322 withoptical image 326 and to modify one or more parameters of the DLengine(s) used for DB Raster, DB Model, near field estimation, andoptical model.

The computer subsystem(s) may perform general wafer inspection and/orpost-processing with DL in three phases. For example, the computersubsystem(s) may train the DL engine(s) with images from selectedsamples/sites on the wafer. In one such example, the computersubsystem(s) may perform offline training 400 as shown in FIG. 4. Thistraining may be performed for selected sites on the wafer. The computersubsystem(s) may then render images with the trained DL engine(s) fromdesign or a is stack of design geometry and/or material for an entiredie or preselected regions/locations. For example, the computersubsystem(s) may perform offline rendering 418 as shown in FIG. 4.Offline rendering may be performed for a whole die or at preselected dielocations. In addition, the computer subsystem(s) may apply dynamiccompensation (e.g., for gain/offset and aberration) in rendering andoptical image comparison for the whole wafer (or sampled dies withpredefined care areas), in real time inspection, or on output imagesfrom inspector or review tool. The computer subsystem(s) may also beconfigured for applying dynamic compensation (for gain/offset andaberration) in a final comparison between a rendered image and an imagefrom an inspection and/or review tool. The computer subsystem(s) may befurther configured for applying dynamic compensation (for gain/offsetand aberration) in a final comparison between a rendered image and animage from the inspection and/or review tool on hot spots and/or smallblocks. For example, as shown in FIG. 4, the computer subsystem(s) mayperform inspection 432. Inspection may be performed online or on imagesoutput from an inspection and/or review tool. In other words, theinspection may or may not be performed online. If the inspection isperformed offline, the inspection may be performed using optical imagesthat were generated by an inspection and/or review tool and were storedin a storage medium by the inspection and/or review tool. The opticalimages may be acquired from the storage medium in any suitable mariner.

“Offline” as that term is used herein is meant to indicate that thestep(s), process(es), flow(s), etc. that are performed offline are notperformed during an inspection of a wafer (e.g., not performed while thewafer is being scanned by an inspection subsystem). In contrast,“online” as that term is used herein is meant to indicate that thestep(s), process(es), flow(s), etc. that are performed online areperformed during an inspection of a wafer (e.g., performed while thewafer is being scanned by an inspection subsystem).

In one such example, in some embodiments, the one or more computersubsystems are configured for training the generative model and/or oneor more additional models used for generating the rendered image basedon: one or more additional rendered images for one or more selectedsites on one or more other wafers generated by performing generating therendered image for the one or more selected sites; and one or moreoptical images generated by the optical inspection subsystem for the oneor more selected sites on the one or more other wafers. For example, asshown in FIG. 4, offline training 400 may use design polygons 402 asinput to DB raster 404, which may be performed as described above. Agray scale image produced by DB raster 404 may be input to DB model 406,which may output a modified gray scale image as described furtherherein. The modified gray scale image produced by DB model 406 may beinput to near field estimation 408, which may produce an estimated nearfield for the wafer as described further herein. The estimated nearfield may be input to optical model 410, which may output an initialrendered image as described further herein. The initial rendered imagemay be input to dynamic compensation 412, which may output renderedimage 414. The computer subsystem(s) may then perform a comparisonbetween the rendered image and an optical image, as shown in step 416.

A cost function may be used to perform the comparison of the renderedimage and the optical image in step 416 and to determine errors (e.g., asum of squared errors (SSE)) in the rendered image compared to theoptical image. The cost function may be configured to use those errorsto train one or more parameters of one or more of the step(s) performedby the computer subsystem(s). For example, the errors can be used totrain one or more parameters of DB model 406 such as the parameters usedfor bias correction and/or corner rounding. In addition, the errors maybe used to train one or more parameters of optical model 410.Furthermore, the arrows between the DB model and the optical model aremeant to indicate that the learning that is performed based on thecomparisons between the rendered image and the optical image may be anonlinear/recursive process for the overall modeling performed by thecomputer subsystem(s). In addition, the errors may be used to adjust oneor more parameters used for dynamic compensation (e.g., to account forday to day drifting in the parameter(s) of the optical inspectionsubsystem which can affect one or more characteristics of the opticalimage on a day to day basis).

In one such embodiment, the initial rendered image is generated for awhole die (or at preselected die locations) in the design printed on thewafer. For example, the computer subsystem(s) may perform offlinerendering 418, as shown in FIG. 4. Such offline rendering may includeusing design polygons 420 as input to DB raster 422, which may beperformed as described above. A gray scale image produced by DB raster422 may be input to DB model 424, which may output a modified gray scaleimage as described further herein. DB model 424 used for offlinerendering may be the DB model trained in offline training 400. Themodified gray scale image produced by DB model 424 may be input to nearfield estimation 426, which may produce an estimated near field for thewafer as described further herein. The estimated near field may be inputto optical model 428, which may output offline rendered image 430, whichmay be an initial rendered image as described further herein. Opticalmodel 428 used for offline rendering may be the optical model that istrained in offline training 400.

The offline rendered image 430 for the whole die may be made up ofmultiple smaller rendered images that in combination span the whole dieon the wafer. For example, the simulation steps described herein may beperformed separately for different portions of a die, and then theresults of the simulation steps may be combined in any manner to producesimulation results for a larger portion of the die (e.g., a subswath ora swath) or for the entire die. Alternatively, the simulation steps maybe performed for all of the design for the whole die such that theresults produced by any one simulation step are fur an entire die on awafer.

In some such embodiments, during the offline training, the cost functionmay be configured for training one or more parameters of one or moregenerative models configured to perform near field estimation 408. Suchtraining may he performed as described further herein. In such anembodiment, during offline rendering, the one or more generative modelswith the trained one or more parameters may be used for near fieldestimation 426.

In additional such embodiments, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform optical model 410. Suchtraining may be performed as described further herein. In such anembodiment, during offline rendering, the one or more generative modelswith the trained one or more parameters may be used for optical model428.

In a further such embodiment, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform near field estimation 408and optical model 410. Such training may be performed as describedfurther herein. In such an embodiment, during offline rendering, the oneor more generative models with the trained one or more parameters may beused for near field estimation 426 and optical model 428.

In some such embodiments, during the offline training, the cost functionmay be configured for training one or more parameters of one or moregenerative models configured to perform DB Model 406. Such training maybe performed as described further herein. In such an embodiment, duringoffline rendering, the one or more generative models with the trainedone or more parameters may be used for DB Model 424.

In another such embodiment, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform DB Raster 404 and DB Model406. Such training may be performed as described further herein. In suchan embodiment, during offline rendering, the one or more generativemodels with the trained one or more parameters may be used for DB Raster422 and DB Model 424.

In still another such embodiment, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform DB Raster 404, DB Model406, near field estimation 408, and optical model 410. Such training maybe performed as described further herein. In such an embodiment, duringoffline rendering, the one or more generative models with the trainedone or more parameters may be used for DB Raster 422, DB Model 424, nearfield estimation 426, and optical model 428.

In one such embodiment, generating the rendered images includesmodifying an initial rendered image to minimize differences between theinitial rendered image and the optical image generated by the opticalinspection subsystem, the initial rendered image is generated offline,and generating the rendered image is performed online. For example, theinitial rendered image may be generated offline as shown in offlinerendering 418 in FIG. 4. In addition, generating the rendered image maybe performed as shown in inspection 432, which may be performed onlinefor the whole wafer. In particular, as shown in FIG. 4, offline renderedimage 434, which may be the offline rendered image generated by offlinerendering 418 (i.e., offline rendered image 430), may be input todynamic compensation 436, which may be the dynamic compensation that wastrained in offline training 400, and which may be performed as describedfurther herein. Dynamic compensation 436 may produce rendered image 438,which may be then compared to an optical image for defect detectionand/or another purpose described herein. Therefore, the dynamiccompensation may modify the initial rendered image that is generatedoffline to minimize differences between the initial rendered image andthe optical image generated by the inspection subsystem therebygenerating the rendered image, and the dynamic compensation may beperformed online (i.e., during an inspection of the wafer).

In another such embodiment, the initial rendered image is generated fora whole die in the design printed on the wafer, and generating therendered image is performed online for an entirety of the wafer. Forexample, as described above, offline rendering 418 may be performed fora whole die on the wafer while inspection 432 may be performed onlinefor an entirety of the wafer meaning that the rendered images must begenerated for an entirety of the wafer (e.g., all of the dies on thewafer).

As described above, in some embodiments, the rendered image may begenerated by estimating the near field of the wafer. In some instances,the near field estimation can be replaced by a near field calculation iffull stack geometry and material information is available for a wafer.In one embodiment, the one or more steps include calculating a nearfield of the wafer based on the information for the design printed onthe wafer, and the information for the design printed on the waferincludes geometry and material characteristics. For example, as shown inFIG. 5, geometry and material information 500 may be input to near fieldcalculation 502. The geometry and material information may be a stack ofgeometry and material information (e.g., in a technology computer aideddesign (TCAD) format) for the wafer. The information for the design usedfor the near field calculation may also include design geometry,material parameters, wavelength and angle dependency, etc. The nearfield calculation may be performed in a number of ways. For example,when the 3D stack of wafer design is known in both geometry andmaterial, the near field at the wafer plane can be calculated by solvingMaxwell's equations (rigorous coupled wave analysis (RCWA) or FDTDmethod). The near field calculation may produce near field 504. Thecalculated near field of the wafer is a simulation of an EM field thatwould be generated at or near the top surface of the wafer (i.e., at thewafer plane) by the interaction of the materials and geometry of thewafer and the light directed to the wafer by the optical inspectionsubsystem. The calculated near field may include amplitude as shown inleft side of calculated near field 504 and phase as shown in right sideof calculated near field 504. The calculated near field may also be orinclude complex numbers.

In one such embodiment, the one or more steps also include generating aninitial rendered image that is another simulation of the image generatedby the optical inspection subsystem for the design printed on the waferbased on the near field and an optical model of the optical inspectionsubsystem. For example, as shown in FIG. 5, optical model 506 may usecalculated near field 504 as input to generate initial rendered image508. The optical model may model with and without aberrations of theoptical inspection subsystem. The initial rendered image may besimulated based on the inspector's optical characteristics using any ofthe approximations, functions, or models described herein.

In a further embodiment, the one or more steps include generating therendered image from the initial rendered image by modifying the initialrendered image to minimize differences between the initial renderedimage and the optical image generated by the optical inspectionsubsystem. For example, as shown in FIG. 5, dynamic compensation 510performed by the computer subsystem(s) may modify initial rendered image508 to generate rendered image 512, one example of which is shown inFIG. 5 as rendered image 514. The dynamic compensation may be performedas described further herein.

As further shown in FIG. 5, optical image 516, one example of which isshown in FIG. 5 as optical image 518, may be acquired by the computersubsystem's) as described herein and compared to rendered image 512.Such comparisons may generate difference image 520. The comparisonsbetween the rendered image and the optical image may be performed fordefect detection, which may be performed as described further herein.The comparisons between the rendered image and the optical image mayalso or alternatively be performed for one or more other purposesdescribed herein.

As described above, one or more generative models may be used to performone or mare steps performed for generating the rendered image. In onesuch example, one or more generative models may he used for near fieldcalculation 502 shown in FIG. 5. In this case, one or more DL enginesmay estimate EM fields on the surface of the wafer as described above.During training and rendering, the dynamic compensation is optional. Inaddition, during training, a cost function (not shown in FIG. 5) such asone of those described further herein may he used to compare renderedimage 512 with optical image 516 and to modify one or more parameters ofthe DL engine(s) used for near field estimation. One or more othermodels may he used for all other steps shown in FIG. 5, and those one ormore other models may or may not be generative models.

In another such example, one or more generative models may he used forestimating optical model 506 shown in FIG. 5. In this case, one or moreDL engines may estimate the optical model of the system as describedabove. During training and rendering, the dynamic compensation isoptional. In addition, during training, a cost function (not shown inFIG. 5) such as one of those described further herein may be used tocompare rendered image 512 with optical image 516 and to modify one ormore parameters of the DL engine(s) used for estimating the opticalmodel. One or more other models may be used for all other steps shown inFIG. 5, and those one or more other models may or may not be generativemodels.

In an additional such example, one or more generative models may be usedfor near field calculation 502 and estimation of optical model 506 shownin FIG. 5. In this case, one or more DL engines simulate the entireimage rendering process. In this manner, steps 502 and 506 shown in FIG.5 may not he performed as separate steps, but may be performed as asingle step whose input is geometry and material information 500 andwhose output is initial rendered image 508. During training andrendering, the dynamic compensation is optional. In addition, duringtraining, a cost function (not shown in FIG. 5) such as one of thosedescribed further herein may be used to compare rendered image 512 withoptical image 516 and to modify one or more parameters of the DLengine(s) used for near field estimation and estimating the opticalmodel.

Like instances in which the computer subsystem(s) estimate the nearfield of the wafer, when the computer subsystem(s) calculate the nearfield, the computer subsystem(s) may perform general wafer inspection inthree phases. For example, the computer subsystem(s) may estimatemodeling parameters from selected sites. In one such example, thecomputer subsystem(s) may perform offline training 600 (for selectedsites) as shown in FIG. 6. The computer subsystem(s) may then renderimages for an entire die or at preselected die locations. For example,as shown in FIG. 6, the computer subsystem(s) may perform offlinerendering 614 (for a whole die on the wafer or at preselected dielocations). In addition, the computer subsystem(s) may apply dynamiccompensation (e.g., for gain offset and aberration) in real timeinspection for the whole wafer (or sampled dies). For example, as shownin FIG. 6, the computer subsystem(s) may perform inspection 624. Theinspection may be performed online or on images output from aninspection and/or review tool as described further herein. Theinspection may be performed for a whole wafer, a whole die, or atpreselected die locations.

In some embodiments, the one or more computer subsystems are configuredfor training the generative model and/or one or more additional modelsused for generating the rendered image based on: one or more additionalrendered images for one or more selected sites on one or more otherwafers generated by performing generating the rendered image for the oneor more selected sites; and one or more optical images generated by theoptical inspection subsystem for the one or more selected sites on theone or more other wafers. For example, as shown in FIG. 6, offlinetraining 600 (for selected sites) may use geometry and materialinformation 602, which may include any such information describedherein, as input to near field calculation 604, which may produce acalculated near field for the wafer as described further herein. Thecalculated near field may be input to optical model 606, which mayoutput an initial rendered image as described further herein. Theinitial rendered image may be input to dynamic compensation 608, whichmay output rendered image 610. The computer subsystem(s) may thenperform a comparison between the rendered image and an optical image, asshown in step 612.

A cost function may he used to perform the comparison of the renderedimage and the optical image in step 612 and to determine errors (e.g., asum of squared errors (SSE)) in the rendered image compared to theoptical image. The cost function may he configured to use those errorsto train one or more parameters of one or more of the step(s) performedby the computer subsystem(s). For example, the cost function can be usedto train one or more parameters of optical model 606. In addition, thecost function may be used to adjust one or more parameters used fordynamic compensation (e.g., to account for day to day drifting in theparameter(s) of the optical inspection subsystem which can affect one ormore characteristics of the optical image on a day to day basis).

In one such embodiment, the initial rendered image is generated for awhole die in the design printed on the wafer. For example, the computersubsystem(s) may perform offline rendering (for a whole die) 614 asshown in FIG. 6. Such offline rendering may is include using geometryand material information 616, which may include any of such informationdescribed herein, as input to near field calculation 618, which mayproduce a calculated near field for the wafer as described furtherherein. The calculated near field may be input, to optical model 620,which may output offline rendered image 622, which may be an initialrendered image as described further herein. Optical model 620 used foroffline rendering 614 may be the optical model that is trained inoffline training 600. The offline rendered image may be otherwisegenerated as described further herein for the whole die.

In some such embodiments, during the offline training, the cost functionmay be configured for training one or more parameters of one or moregenerative models configured to perform near field calculation 604 (orestimation). Such training may he performed as described further herein.In such an embodiment, during offline rendering, the one or moregenerative models with the trained one or more parameters may be usedfor near field calculation 618 (or estimation).

In additional such embodiments, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform estimation of optical model606. Such training may be performed as described further herein. In suchan embodiment, during offline rendering, the one or more generativemodels with the trained one or more parameters may be used for opticalmodel 620 estimation.

In another such embodiment, during the offline training, the costfunction may be configured for training one or more parameters of one ormore generative models configured to perform near field calculation 604(or estimation) and estimation of optical model 606. Such training maybe performed as described further herein. In such an embodiment, duringoffline rendering, the one or more generative models with the trainedone or more parameters may be used for near field estimation 618 andestimation of optical model 620.

In one such embodiment, generating the rendered images includesmodifying an initial rendered image to minimize differences between theinitial rendered image and the optical image generated by the opticalinspection subsystem, the initial rendered image is generated offline,and generating the rendered image is performed online. For example, theinitial rendered image may be generated offline as shown in offlinerendering 614 in FIG. 6. In addition, generating the rendered image maybe performed as shown in inspection 624, which may be performed onlinefor the whole wafer. In particular, as shown in FIG. 6, offline renderedimage 626, which may be the offline rendered image generated by offlinerendering 614 (i.e., offline rendered image 622) may be input to dynamiccompensation 628, which may be the dynamic compensation that was trainedin offline training 600, and which may be performed as described furtherherein. Dynamic compensation 628 may produce rendered image 630, whichmay he then compared to an optical image for defect detection and/or anyother purposes described herein. Therefore, the dynamic compensation maymodify the initial rendered image that is generated offline to minimizedifferences between the initial rendered image and the optical imagegenerated by the inspection subsystem thereby generating the renderedimage, and the dynamic compensation may be performed online (i.e.,during an inspection of the wafer). In this manner, generating therendered image may be performed online.

In another such embodiment, the initial rendered image is generated fora whole die in the design printed on the wafer, and generating therendered image is performed online for an entirety of the wafer. Forexample, as described above, offline rendering 614 may be performed fora whole die on the wafer while inspection 624 may be performed onlinefor an entirety of the wafer, meaning that the rendered images may begenerated for an entirety of the wafer (e.g., all of the dies on thewafer).

In one embodiment, generating the rendered image is performed for onlyone or more areas in the design printed on the wafer such thatgenerating the rendered image is not performed for an entirety of thedesign. The areas(s) are also referred to herein as “hot spots” or“small blocks.” Areas, hot spots, and small blocks as those terms areused herein can be defined as a unit of the circuit structure thatrepeats many times on a die. An example size of the areas, hot spots,and small blocks is about 500 nm by about 500 nm. In this manner, theembodiments described herein may perform the steps described herein foronly some portions, but not an entirety, of a die. The hot spots and/orsmall blocks may be identified and/or selected in any suitable manner.For example, the hot spots and/or small blocks may be care areas in thedesign in which inspection is to be performed. In addition, oralternatively, the hot spots and/or small blocks may be relatively smallportions of the design that are repeated two or more times within thedesign. In this manner, generating the initial rendered image may beperformed for only one small block, but that same initial rendered imagemay be dynamically compensated (possibly in different manners) and thencompared to multiple optical images generated at different instances ofthe same small block of the design printed on the wafer. Generating therendered image for only one or more areas may otherwise be performed asdescribed herein (e.g., as shown in FIGS. 3 and 5).

The computer subsystem(s) may be configured for hot spot or small blockwafer inspection in three phases. For example, the computer subsystem(s)may estimate modeling parameters from selected sites. In one suchexample, the computer subsystem(s) may perform offline training 400shown in FIG. 4 and 600 shown in FIG. 6 in which the selected sites arereplaced with selected hot spot and/or small block sites. The computersubsystem(s) may also render images for all hot spots/small blocks forone die. For example, the computer subsystem(s) may perform offlinerendering 418 as shown in FIG. 4 and 614 as shown in FIG. 6, but insteadof performing the offline rendering for the whole die, the offlinerendering is performed for all hot spots and/or small blocks in one die.In addition, the computer subsystem(s) may apply dynamic compensationfor gain/offset and aberration) in real time or offline inspection ofall hot spots/small blocks in a whole wafer (or sampled dies). Forexample, the computer subsystem(s) may be configured for performinginspection 432 as shown in FIG. 4 and 624 as shown in FIG. 6 except thatinstead of the inspection being performed for the whole wafer, theinspection is performed for only hot spots and/or small blocks on thewhole wafer.

In an embodiment, the computer subsystem(s) are configured for trainingthe generative model and/or one or more additional models used forgenerating the rendered image based on: two or more additional renderedimages for two or more areas on one or more other wafers generated byperforming generating the rendered image for the two or more areas; andtwo or more optical images generated by the optical inspection subsystemfor the two or more areas on the one or more other wafers. These stepsmay be performed as described herein (as shown in offline training 400shown in FIG. 4 and offline training 600 shown in FIG. 6), where theselected sites are the two or more areas (e.g., two or more hot spotsand/or small blocks).

The training performed for a first of the two or more areas is performeddifferently than the training performed for a second of the two or moreareas. In this manner, if the interesting area for inspection orpost-processing is for hot spots, or small blocks (having an examplesize of about 300 nm×about 300 nm) that repeat many times within a die,the DL engine(s) and the other models) described herein may becustomized for each hot spot/small block type or groups of types. Forexample, if a first hot spot includes a first portion of the designhaving first characteristics (e.g., dense lines) and a second hot spotincludes a second portion of the design having second characteristics(e.g., sparse contact holes) that are different from the firstcharacteristics, the first and second hot spots may be printed on thewafer differently (e.g., with different bias and corner rounding) andmay be imaged by the optical inspection subsystem differently (e.g.,with different resolution, contrast, etc.). Therefore, one or moremodels (e.g., the DB model and/or the optical model) used for generatingrendered images for the different hot spots would preferably becustomized to account for such differences in the processes involved ingenerating actual optical images of the different hot spots. Therefore,the offline training may generate hot spot (or small block) specificmodel(s) based on rendered image(s) and optical image(s) that correspondto the hot spot (or small Hock) for which the model(s) are beingtrained. Such training may otherwise be performed as described herein.

In one such embodiment, generating the rendered image includes modifyingan initial rendered image to minimize differences between the initialrendered image and the optical image generated by the optical inspectionsubsystem, the initial rendered image is generated offline, andgenerating the rendered image is performed online. These steps may beperformed as described further herein and shown in FIGS. 4 and 6. Forexample, the offline rendering shown in FIGS. 4 and 6 may be performedfor hot spots and/or small blocks on one die or preselected locations,and the inspection shown in FIGS. 4 and 6 may be performed for hot spotsand/or small blocks or on images output from an inspection and/or reviewtool.

In another such embodiment, the initial rendered image is generated forall of the two or more areas in a die in the design printed on thewafer, and generating the rendered image is further performed online forall of the two or more areas in an entirety of the wafer. For example,as described above, the offline rendering of the initial rendered imagemay be performed for all hot spots and/or small blocks on one die. Inaddition, as described further herein, the online inspection may beperformed for all hot spots and/or small blocks on a whole wafer.

The infrastructure for software and hardware that performs thepreparation, setup and inspection (or final comparison) described hereinmay be configured in a variety of different ways. For example, in oneembodiment, the one or more computer subsystems include two or morecomputer subsystems, and at least one of the two or more computersubsystems is not part of a tool that includes the optical inspectionsubsystem. In this manner, the computer subsystem(s) may include atleast one computer subsystem that is not part of the optical inspectiontool (e.g., computer subsystem 102 shown in FIG. 1) and at least onecomputer subsystem that is part of the optical inspection tool (e.g.,computer subsystem 36 shown in FIG. 1). Such a configuration may beadvantageous when it is more suitable to perform some of the step(s)described herein offline (e.g., using computer subsystem 102) Whileother steps are more suitably performed online (e.g., using computersubsystem 36).

In a further embodiment, the one or more computer subsystems include atleast one virtual inspection system also commonly referred to as avirtual inspector (VI). A VI can be generally defined as a computersystem that can store massive amounts of output generated for a wafer byan inspection subsystem such that the output can be “played back” in amanner that mimics real time acquisition of the output during which avirtual inspection can be performed for the wafer using only the storedoutput. Examples of such virtual inspectors are illustrated in U.S. Pat.No. 8,126,255 issued on Feb. 28, 2012 to Bhaskar et al. and U.S. Pat.No. 9,222,895 issued on Dec. 29, 2015 to Duffy et al., which areincorporated by reference as if fully set forth herein. The computersubsystem(s) described herein may be further configured as described inthese patents.

In the embodiments described herein, a VI may be particularly useful forperforming one or more offline steps during setup and/or for storage ofthe various information and/or images generated and/or used in theembodiments described herein. For example, a VI may be particularlyuseful for setup of the die-to-DB inspection described herein. In onesuch example, a VI may be configured for extracting design clips (i.e.,relatively small portions of an entire design of a die on a wafer) froma design DB or file. In addition, a VI may be configured for generatingthe initial rendered images described herein and storing the initialrendered images. In addition, since the VI may be capable of storingmassive amounts of images generated for physical wafers (i.e., actualwafers), the VI may be particularly suitable for tuning one or moreparameters of the generative model(s) and/or one or more of theadditional models described herein using both simulated images andactual wafer images.

A VI may also be configured for performing one or more of the stepsdescribed herein online. For example, a VI may be configured to performthe die-to-DB inspection (i.e., the image compare and defect detectionsteps described herein) online (and offline as well). Furthermore, sincea VI may include multiple image computer nodes, performance of any ofthe steps described herein can be distributed across the multiple imagecomputer nodes thereby providing advantages for throughput. Furthermore,the rendered images may be stored on the VI and then transferred to oneor more other computer subsystems (e.g., a computer subsystem that ispart of an optical inspection tool) as those other computer subsystem(s)need the rendered images (e.g., for online optical die-to-DBinspection).

The computer subsystem(s) may include a prep station for training and aprocessing unit for rendering. The prep station and the processing unitcan be the same physical unit or separate units. The computersubsystem(s) may also include a prep station for design clips and imagerendering preparation, with single or multiple cores of CPU/GPU/FPGAcluster and storage. For an inspection use case, the processing unit maybe the inspection tool (e.g., a combination of an optical inspectionsubsystem and at least one computer subsystem coupled to the opticalinspection subsystem). For the post processing use case, the processingunit can be single or multiple cores of CPU/GPU/FPGA cluster andstorage, which may be the same or different than the prep station. Inaddition, the computer subsystems may be coupled by a network betweenthe prep station and processing unit (e.g., a computer subsystem of theinspection tool) to transfer rendered images. The computer subsystem(s)may also include a design/TCAD based offline image rendering engine onthe prep station or the inspection tool/processing unit. The computersubsystem(s) may further include a design/TCAD based offline trainingengine on the prep station or the inspection tool/processing unit. Theprep station, processing unit, and/or inspection tool hardware can beoptimized to speed up the DL engine(s).

In some such embodiments, the infrastructure used for preparing thedesign DB may include a DB in which design information is stored (e.g.,one or more reticle design files (RDFS)) and a server coupled to the DBand image computer(s) and/or VI(s). The server and-/or the imagecomputer(s) and/or VI(s) may extract design clips from the DB therebypreparing the design DB for use by the embodiments described herein. Theimage computer(s) and/or the VI(s) may store the extracted design clipsin a design clip DB, which may have any suitable format known in theart.

The infrastructure used for design rendering may include the imagecomputer(s) and/or VI(s) of the infrastructure configured for preparingthe design data. The image computer(s) and/or VI(s) may be configured torender the design (e.g., from the design clips stored in the design clipDB) to generate the rendered images described herein. In addition, theimage computer(s) and/or the VI(s) may store the rendered images in arendered image DB, which may have any suitable format known in the art.

The infrastructure configured for performing the die-to-DB inspectionmay include image computer(s), which may be different from the imagecomputer(s) included in the infrastructure configured for preparing thedesign DB and the image computer(s) included in the infrastructureconfigured for design rendering. The image computer(s) included in theinfrastructure for performing the die-to-DB inspection may be configuredto acquire the rendered images from the rendered image DB. These imagecomputer(s) may also acquire the optical images generated by the opticalinspection subsystem and perform one or more steps using the renderedand optical images such as pixel-to-design alignment (PDA) and defectdetection. In this manner, the image computer(s) may perform inspectionwith a rendered die image.

The infrastructure configured for the inspection may also include one ormore user interfaces (UIs) coupled to the image computer(s) such thatthe results produced by the image computer(s) can be provided to a userthrough the one or more UIs and/or such that input and/or instructionscan be received from the user through the one or more The UI(s) mayinclude any suitable tills known in the art (e.g., such as a UI that isused by commercially available inspection tools and configured to havethe capability described herein).

Each of the embodiments of the system described herein may be furtherconfigured according to any other embodiment(s) described herein. Eachof the embodiments described herein may also be further configured asdescribed in U.S. patent application Ser. No. 15/088,081 by Wells et al.filed Mar. 31, 2016, Ser. No. 15/176,139 by Zhang et al. filed Jun. 7,2016, and Ser. No. 15/353,210 by Bhaskar et al. filed Nov. 16, 2016,which are incorporated by reference as if fully set forth herein.

Another embodiment relates to a computer-implemented method fordetecting defects on a wafer. The method includes steps for each of thefunctions of the computer subsystem(s) described above. The opticalinspection subsystem is configured as described herein.

Each of the steps of the method may be performed as described furtherherein. The method may also include any other step(s) that can beperformed by the inspection subsystem and or computer subsystem(s) orsystem(s) described herein. The steps of the method are performed by oneor more computer systems, which may be configured according to any ofthe embodiments described herein. In addition, the method describedabove may be performed by any of the system embodiments describedherein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for detecting defects on awafer. One such embodiment is shown in FIG. 7. In particular, as shownin FIG. 7, non-transitory computer-readable medium 700 includes programinstructions 702 executable on computer system 704. Thecomputer-implemented method may include any step(s) of any method(s)described herein.

Program instructions 702 implementing methods such as those describedherein may be stored on computer-readable medium 700. Thecomputer-readable medium may be a storage medium such as a magnetic oroptical disk, a magnetic tape, or any other suitable non-transitorycomputer-readable medium known in the art.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (“MFC”), SSE (Streaming SIMDExtension) or other technologies or methodologies, as desired.

Computer system 704 may be configured according to any of theembodiments described herein.

All of the methods described herein may include storing results of oneor more steps of the method embodiments in a computer-readable storagemedium. The results may include any of the results described herein andmay be stored in any manner known in the art. The storage medium mayinclude any storage medium described herein or any other suitablestorage medium known in the art. After the results have been stored, theresults can be accessed in the storage medium and used by any of themethod or system embodiments described herein, formatted for display toa user, used by another software module, method, or system, etc.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. For example, methods and systems for detecting defectson a wafer are provided. Accordingly, this description is to beconstrued as illustrative only and is for the purpose of teaching thoseskilled in the art the general manner of carrying out the invention. Itis to be understood that the forms of the invention shown and describedherein are to be taken as the presently preferred embodiments. Elementsand materials may be substituted for those illustrated and describedherein, parts and processes may be reversed, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims.

What is claimed is:
 1. A system configured to detect defects on a wafer, comprising: an optical inspection subsystem comprising at least a light source and a detector, wherein the light source is configured to generate light that is directed to a wafer, and wherein the detector is configured to detect light from the wafer and to generate images responsive to the detected light; and one or more computer subsystems configured for: generating a rendered image based on information for a design printed on the wafer, wherein the rendered image is a simulation of an image generated by the optical inspection subsystem for the design printed on the wafer, wherein generating the rendered image comprises one or more steps, and wherein the one or more computer subsystems are further configured for performing at least one of the one or more steps by executing a generative model; comparing the rendered image to an optical image of the wafer generated by the optical inspection subsystem, wherein the design is printed on the wafer using a reticle; and detecting defects on the wafer based on results of the comparing.
 2. The system of claim 1, wherein the at least one step is further performed by executing the generative model and an additional generative model.
 3. The system of claim 2, wherein the one or more steps comprise a first step and a second step, and Wherein the one or more computer subsystems are further configured for performing the first step by executing the generative model and performing the second step by executing the additional generative model.
 4. The system of claim 2, wherein the one or more computer subsystems are further configured for separately training the generative model and the additional generative model.
 5. The system of claim 1, wherein the generative model is configured as a convolutional neural network.
 6. The system of claim 1, wherein the generative model is configured as an auto-encoder.
 7. The system of claim 1, wherein the one or more steps comprise converting polygons in the information for the design to a gray scale image.
 8. The system of claim 7, wherein said converting the polygons is performed with sub-pixel accuracy.
 9. The system of claim 7, wherein the one or more steps further comprise generating a modified gray scale image by applying bias correction and corner rounding to the gray scale image.
 10. The system of claim 9, wherein the one or more steps further comprise estimating a near field of the wafer based on the modified gray scale image and the information for the design printed on the wafer.
 11. The system of claim 10, wherein the one or more steps further comprise generating an initial rendered image that is another simulation of the image generated by the optical inspection subsystem for the design printed on the wafer based on the near field and an optical model of the optical inspection subsystem.
 12. The system of claim 11, wherein the one or more steps further comprise generating the rendered image from the initial rendered image by modifying the initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem.
 13. The system of claim 1, wherein the one or more steps comprise calculating a near field of the wafer based on the information for the design printed on the wafer, and wherein the information for the design printed on the wafer comprises geometry and material characteristics.
 14. The system of claim 13, wherein the one or more steps further comprise generating an initial rendered image that is another simulation of the image generated by the optical inspection subsystem for the design printed on the wafer based on the near field and an optical model of the optical inspection subsystem.
 15. The system of claim 14, wherein the one or more steps further comprise generating the rendered image from the initial rendered image by modifying the initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem.
 16. The system of claim 1, wherein the one or more computer subsystems are further configured for training the generative model based on: one or more additional rendered images for one or more selected sites on one or more other wafers generated by performing said generating for the one or more selected sites; and one or more optical images generated by the optical inspection subsystem for the one or more selected sites on the one or more other wafers.
 17. The system of claim 16, wherein generating the rendered image further comprises modifying an initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem, wherein the initial rendered image is generated offline, and wherein generating the rendered image is performed online.
 18. The system of claim 17, wherein the initial rendered image is generated for a whole die in the design printed on the wafer, and wherein generating the rendered image is further performed online for an entirety of the wafer.
 19. The system of claim 1, wherein the one or more computer subsystems are further configured for training one or more additional models used for said generating based on: one or more additional rendered images for one or more selected sites on one or more other wafers generated by performing said generating for the one or more selected sites; and one or more optical images generated by the optical inspection subsystem for the one or more selected sites on the one or more other wafers.
 20. The system of claim 19, wherein generating the rendered image further comprises modifying an initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem, wherein the initial rendered image is generated offline, and wherein generating the rendered image is performed online.
 21. The system of claim 20, wherein the initial rendered image is generated for a whole die in the design printed on the wafer, and wherein generating the rendered image is further performed online for an entirety of the wafer.
 22. The system of claim 1, wherein said generating is performed for only one or more areas in the design printed on the wafer such that said generating is not performed for an entirety of the design.
 23. The system of claim 1, wherein the one or more computer subsystems are further configured for training the generative model based on: two or more additional rendered images for two or more areas on one or more other wafers generated by performing said generating for the two or more areas; and two or more optical images generated by the optical inspection subsystem for the two or more areas on the one or more other wafers, and wherein said training performed for a first of the two or more areas is performed differently than said training performed for a second of the two or more areas.
 24. The system of claim 23, wherein generating the rendered image further comprises modifying an initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem, wherein the initial rendered image is generated offline, and wherein generating the rendered image is performed online.
 25. The system of claim 24, wherein the initial rendered image is generated for all of the two or more areas in a die in the design printed on the wafer, and wherein generating the rendered image is further performed online for all of the two or more areas in an entirety of the wafer.
 26. The system of claim 1, wherein the one or more computer subsystems are further configured for training one or more additional models used for said generating based on: two or more additional rendered images for two or more areas on one or more other wafers generated by performing said generating for the two or more areas; and two or more optical images generated by the optical inspection subsystem for the two or more areas on the one or more other wafers, and wherein said training performed for a first of the two or more areas is performed differently than said training performed for a second of the two or more areas.
 27. The system of claim 26, wherein generating the rendered image further comprises modifying an initial rendered image to minimize differences between the initial rendered image and the optical image generated by the optical inspection subsystem, wherein the initial rendered image is generated offline, and wherein generating the rendered image is performed online.
 28. The system of claim 27, wherein the initial rendered image is generated for all of the two or more areas in a die in the design printed on the wafer, and wherein generating the rendered image is further performed online for all of the two or more areas in an entirety of the wafer.
 29. The system of claim 1, wherein the one or more computer subsystems comprise two or more computer subsystems, and wherein at least one of the two or more computer subsystems is not part of a tool that includes the optical inspection subsystem.
 30. The system of claim 1, wherein the one or more computer subsystems comprise at least one virtual inspection system.
 31. The system of claim 1, wherein the one or more computer subsystems are further configured for determining if the reticle passes qualification based on the detected defects.
 32. The system of claim 1, wherein the one or more computer subsystems are further configured for determining a performance of one or more process steps performed on the wafer based on the detected defects.
 33. The system of claim 32, wherein the wafer is a short loop wafer.
 34. A non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for detecting defects on a wafer, wherein the computer-implemented method comprises: generating a rendered image based on information for a design printed on a wafer, wherein the rendered image is a simulation of an image generated by an optical inspection subsystem for the design printed on the wafer, wherein the optical inspection subsystem comprises at least a light source and a detector, wherein the light source is configured to generate light that is directed to the wafer, wherein the detector is configured to detect light from the wafer and to generate images responsive to the detected light, wherein generating the rendered image comprises one or more steps, and wherein at least one of the one or more steps is performed by executing a generative model; comparing the rendered image to an optical image of the wafer generated by the optical inspection subsystem, wherein the design is printed on the wafer using a reticle; and detecting defects on the wafer based on results of the comparing, wherein said generating, said comparing, and said detecting are performed by one or more computer subsystems.
 35. A computer-implemented method for detecting defects on a wafer, comprising: generating a rendered image based on information for a design printed on a wafer, wherein the rendered image is a simulation of an image generated by an optical inspection subsystem for the design printed on the wafer, wherein the optical inspection subsystem comprises at least a light source and a detector, wherein the light source is configured to generate light that is directed to the wafer, wherein the detector is configured to detect light from the wafer and to generate images responsive to the detected light, wherein generating the rendered image comprises one or more steps, and wherein at least one of the one or more steps is performed by executing a generative model; comparing the rendered image to an optical image of the wafer generated by the optical inspection subsystem, wherein the design is printed on the wafer using a reticle; and detecting defects on the wafer based on results of the comparing, wherein said generating, said comparing, and said detecting are performed by one or more computer subsystems. 